You do not need to have a tech background! I know many analysts and engineers with psychology, education, linguistics, etc backgrounds and they are HIGH performers. People with non-STEM backgrounds tend to have a different perspective of how to think about tech problems and that perspective has lots of potential to make a huge impact (and profit) in the tech world. I recommend transitioning into a data analytics role that is located in the industry that you have experience in. Ex: If you have experience with HR, then go look for HR data analytics positions. Come from car sales, go look at sales operation data analyst positions or data analytics positions at car manufacturers. If you are aiming to transition into a data role that’s in an industry that you don’t have experience in, I recommend working on projects that use data from that industry to count as “experience” on your resume.
NO! However, if you don’t have a degree, I do recommend getting certified in a specialized data tool, depending on which job role you're interested in (Tableau, Power BI, Azure, Snowflake, etc). The certification will help with filling the educational component of your resume and give recruiters and hiring managers more confidence in your skills. I recommend getting the entry level cert for whatever tool it is you choose, as it will give you the foundational knowledge you need to understand the technology. Just remember, certs don’t provide you with experience so you should be doing projects and hands-on labs as you're studying. Doing projects will give you experience AND confidence that you're learning and retaining the information you've been studying.
In general, you do not need a strong background in math. I do recommend having a basic understanding of algebra, probability, and statistics concepts as it will help you with any analysis you’re doing. Most analysis uses basic algorithms and equations but once you enter data science/machine learning, artificial intelligence territory, you will need stronger knowledge. Keep in mind, many tools and languages do the heavy lifting for you since you won’t have to calculate anything by hand.
You do not need coding skills for most entry/mid level data roles as you are typically using some data analytics tool like Tableau, Excel, or Power BI. However, it is seen as a bonus by recruiters and hiring managers if you are familiar with R or Python. Coding should be a side topic for you to study for a ddta analyst role. If you’re looking to get into Data Engineering or Data Scientist roles, then you do need to have coding knowledge as much or all of the job revolves around code.
Yes you absolutely need to learn SQL! It is basically the language of data and it is extremely difficult to not find it listed on a data job description. SQL basics are not too difficult to learn because you’re using it to talk to a database or warehouse vs building an application. You can find many SQL videos for data analysts on Youtube and Udemy. If you’re going for a data engineering role, then you will need intermediate/advanced SQL knowledge because most of the work you’re doing involving managing the data in databases and warehouses.
It’s never too late. Data is becoming a more popular field to get into because the world is becoming more online. The more online everything is, the more data that is created. The more data created, the more urgency there is for companies to understand and capitalize off of the data they have. Companies are desperate to find SMEs that can help them store, process, and analyze their data so that they can remain competitive and generate revenue. There will be no shortage of data jobs. There are actually more jobs than people available to fill those roles.
Business analyst: They focus on analyzing processes, systems, problems and give suggestions on how to improve them. This is definitely more of a functional role. They receive data/dashboards from a data analyst and use that information to give suggestions to stakeholders on how to make improvements. Keep in mind that this role is becoming more technical, so some companies might require a business analyst to have some analysis skills.
Data Analyst: They focus on creating dashboards that give stakeholders(business analysts and managers) insight on how to solve a business problem. This is a technical role that revolves around cleaning and analyzing data.
Data Engineer: They gather and clean data from multiple sources/applications and store that data in one centralized place, typically called a data warehouse. This role is highly technical as it involves programming.
Data Scientists: They gather and clean data from multiple sources, then use the data to predict future outcomes. This role is highly technical as it involves programming and advanced mathematics.
Business acumen is subject matter knowledge, aka knowledge about the industry that you’re in. This means that you understand the ins and outs of the industry, including trends in the industry, certain words and acronyms used, popular tools, how people make money in the industry, common issues that occur, and the type of analysis commonly looked for by companies in the industry. You use this knowledge to inform your thought process around conversations with stakeholders and job responsiblities. Your level of business acumen plays a huge role in how successful you are at your job. It’d not a substitute for the technical knowledge you need to perform your job, but a complement to those skills.
I tell people to start with Tableau or PowerBI since those are in-demand and do not have a steep learning curve. All data visualization tools have the same goal: to help people analyze their data. It’s similar to choosing which gas station to go to…all gas stations sell gas, but they offer different pricing, snacks, and features (car wash, air). Just pick one and go. The biggest things to focus on are understanding how to use the tool, why the tool is used, creating projects with the tool, and possibly getting certified.
Pick one of the popular cloud providers and just start learning. All of the cloud providers offer the same data services to a certain extent, the services just have different names. I would focus on learning the fundamentals of the cloud’s data stack, that way there won’t be a steep learning curve if you do need to switch to another cloud providers adjacent tool. The skills needed are the same. Ex: Learning Amazon Redshift and applying for a job that uses GCP BigQuery. If you already know the fundamentals of Data Warehousing and SQL, BigQuery will be relatively easy to understand and use. The quickest way to learn all the necessary services in the data stack is by getting a Udemy or vendor created course.
Entry level business analyst salary: $60-70k (USD)
Entry level data analyst salary: $65-85k (USD)
Entry level data engineer salary: $85-95k (USD)
Entry level data scientist salary: $90-100k (USD)
Short answer, no. You will need to work on industry specific projects and add them to your resume. The GDA certifification course gives you great foundational knowledge but it’s not enough to understand how data analysts will work on the job. Do some more SQL work, create some more dashboards, do your own projects. That will help you stand out to recruiters in a sea of people who have the same projects and certification listed on their resume.
There is tons of free content on Youtube. You can also take courses on Coursera, Udemy, PluralSight, LinkedIn Learning, and Datacamp. There are tons more that I didn’t mention. Either way, I recommend checking the reviews and comments for any content you make just to see if it is good/recommended by others.
